Load Libraries

require(tidyverse)
## Loading required package: tidyverse
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## v tidyr   1.1.4     v stringr 1.4.0
## v readr   2.0.2     v forcats 0.5.1
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## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
require(lubridate)
## Loading required package: lubridate
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## Attaching package: 'lubridate'
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require(stringr)
require(readxl)
## Loading required package: readxl
require(arsenal)
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## Attaching package: 'arsenal'
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require(gtsummary)
## Loading required package: gtsummary
require(plotly)
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## Attaching package: 'plotly'
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Load From file

needs <- read.csv("../Data/Cleaned/01_Needs.csv")
names(needs) <- toupper(names(needs))
referrals <- read.csv("../Data/Cleaned/01_referrals.csv")
names(referrals) <- toupper(names(referrals))

Type Covert

needs <- needs %>% mutate(AGE = as.factor(AGE), Gender = as.factor(GENDER), LANGUAGE = as.factor(LANGUAGE)
                          ,MILITARY_STATUS = as.factor(MILITARY_STATUS)
                          ,DISABILITY_STATUS = as.factor(DISABILITY_STATUS)
                          ,DISABILITY_TYPE = as.factor(DISABILITY_TYPE)
                          ,HEALTH_INSURANCE = as.factor(HEALTH_INSURANCE)
                          );

referrals <- referrals %>% mutate(AGE = as.factor(AGE), Gender = as.factor(GENDER), Language = as.factor(LANGUAGE)
                          ,MILITARY_STATUS = as.factor(MILITARY_STATUS)
                          ,DISABILITY_STATUS = as.factor(DISABILITY_STATUS)
                          ,DISABILITY_TYPE = as.factor(DISABILITY_TYPE)
                          ,HEALTH_INSURANCE = as.factor(HEALTH_INSURANCE))

Generate Stats

needs %>% select(NEED) %>% tbl_summary()
Characteristic N = 26,0951
NEED
Arts Culture and Recreation 2 (<0.1%)
Clothing/Personal/Household Needs 112 (0.4%)
Disaster Services 181 (0.7%)
Education 10 (<0.1%)
Employment 241 (0.9%)
Food/Meals 737 (2.8%)
Health Care 285 (1.1%)
Housing 10,915 (42%)
Income Support/Assistance 285 (1.1%)
Individual Family and Community Support 285 (1.1%)
Information Services 11,931 (46%)
Legal Consumer and Public Safety Services 283 (1.1%)
Mental Health/Addictions 493 (1.9%)
Other Government/Economic Services 39 (0.1%)
Transportation 123 (0.5%)
Utility Assistance 77 (0.3%)
Volunteers/Donations 96 (0.4%)

1 n (%)

NEED : Replace non housing and non information services with others

needs <- needs %>% mutate(NEED = if_else(NEED != 'Housing' & NEED != 'Information Services','Others',NEED))
needs$NEED <- as.factor(needs$NEED)

Generate Stats

needs %>% select(CE_SCREENED) %>% tbl_summary()
Characteristic N = 26,0951
CE_SCREENED
DV Referral 920 (3.5%)
Literally Homeless 19,621 (75%)
Precariously Housed 359 (1.4%)
Risk of Homelessness 5,056 (19%)
ROI Declined 100 (0.4%)
Unknown 39

1 n (%)

needs %>% select(NEED,CE_SCREENED) %>% tbl_summary(by = NEED )
Characteristic Housing, N = 10,9151 Information Services, N = 11,9311 Others, N = 3,2491
CE_SCREENED
DV Referral 581 (5.3%) 85 (0.7%) 254 (7.8%)
Literally Homeless 8,077 (74%) 9,304 (78%) 2,240 (69%)
Precariously Housed 124 (1.1%) 178 (1.5%) 57 (1.8%)
Risk of Homelessness 2,064 (19%) 2,328 (20%) 664 (20%)
ROI Declined 48 (0.4%) 24 (0.2%) 28 (0.9%)
Unknown 21 12 6

1 n (%)

needs %>% select(NEED = NEED,NEED_1 = NEED) %>% tbl_summary(by = NEED)
Characteristic Housing, N = 10,9151 Information Services, N = 11,9311 Others, N = 3,2491
NEED_1
Housing 10,915 (100%) 0 (0%) 0 (0%)
Information Services 0 (0%) 11,931 (100%) 0 (0%)
Others 0 (0%) 0 (0%) 3,249 (100%)

1 n (%)

Q1 —-

  1. Frequency- needs (% of clients indicating each need)
tab1 <- tableby(~NEED,data = needs %>% select(NEED,CONTACT_NUMBER) %>% distinct())
q1.a <- as.data.frame(tab1)
summary(tab1)
## 
## 
## |                                       | Overall (N=16696) |
## |:--------------------------------------|:-----------------:|
## |**NEED**                               |                   |
## |&nbsp;&nbsp;&nbsp;Housing              |   6946 (41.6%)    |
## |&nbsp;&nbsp;&nbsp;Information Services |   8840 (52.9%)    |
## |&nbsp;&nbsp;&nbsp;Others               |    910 (5.5%)     |
needs %>% select(NEED,CONTACT_NUMBER) %>% distinct() %>% select(NEED) %>% tbl_summary()
Characteristic N = 16,6961
NEED
Housing 6,946 (42%)
Information Services 8,840 (53%)
Others 910 (5.5%)

1 n (%)

q1.a.plt <- needs %>%select(NEED,CONTACT_NUMBER) %>% distinct() %>% group_by(NEED) %>% summarise(count=n())
fig <- plot_ly(data = q1.a.plt,x=~NEED,y=~count,type = 'bar')
fig <- fig %>% layout(yaxis = list(title = 'Count'), barmode = 'group')
fig
  1. Frequency- number of needs per client
q1.b <- needs %>% select(CONTACT_NUMBER,NEED) %>% distinct() %>% group_by(CONTACT_NUMBER) %>% summarise(NUMBER_OF_NEEDS = n())
q1.b_2 <- q1.b %>% group_by(NUMBER_OF_NEEDS) %>% summarise(NUMBER_OF_CLIENTS = n()) 
q1.b_2
## # A tibble: 3 x 2
##   NUMBER_OF_NEEDS NUMBER_OF_CLIENTS
##             <int>             <int>
## 1               1             12666
## 2               2              1790
## 3               3               150
  1. Frequency- CE Screened (all categories) filtered by Need=Housing
    • if Contact Number has Housing and Information Services Need, include in this frequency
q1.c <- needs %>% filter(NEED == 'Housing' | NEED == 'Information Services') %>% distinct(CONTACT_NUMBER,CE_SCREENED,NEED)
q1.c %>% select(CE_SCREENED,NEED) %>% tbl_summary(by = CE_SCREENED,missing = "no") %>% modify_table_body(filter,label %in% c('Housing','Information Services'))
## 17 observations missing `CE_SCREENED` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `CE_SCREENED` column before passing to `tbl_summary()`.
Characteristic DV Referral, N = 4101 Literally Homeless, N = 12,7341 Precariously Housed, N = 1771 Risk of Homelessness, N = 2,6441 ROI Declined, N = 481
Housing 366 (89%) 5,517 (43%) 62 (35%) 1,112 (42%) 32 (67%)
Information Services 44 (11%) 7,217 (57%) 115 (65%) 1,532 (58%) 16 (33%)

1 n (%)

  1. Frequency- CE Screened (all categories) filtered by Need=Information Services -if Contact Number has Housing and Information Services Need, exclude from this frequency
q1.d <- needs %>% filter(!(CONTACT_NUMBER %in% q1.c$CONTACT_NUMBER)) %>% distinct(CONTACT_NUMBER,CE_SCREENED,NEED)

q1.d %>% select(CE_SCREENED,NEED) %>% tbl_summary(by = CE_SCREENED,missing = "no") %>% modify_table_body(filter,!(label %in% c('Housing','Information Services')))
## 3 observations missing `CE_SCREENED` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `CE_SCREENED` column before passing to `tbl_summary()`.
Characteristic DV Referral, N = 241 Literally Homeless, N = 1401 Precariously Housed, N = 21 Risk of Homelessness, N = 261 ROI Declined, N = 21
NEED
Others 24 (100%) 140 (100%) 2 (100%) 26 (100%) 2 (100%)

1 n (%)

  1. Crosstab: CE Screened (all categories) filtered by Need=Housing x Gender
q1.e <- needs %>% filter(NEED == 'Housing') %>% select(CONTACT_NUMBER, CE_SCREENED, GENDER) %>% distinct()
q1.e %>% select(CE_SCREENED,GENDER) %>% tbl_summary(by = GENDER,missing = "no")
## 131 observations missing `GENDER` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `GENDER` column before passing to `tbl_summary()`.
Characteristic Female, N = 4,3341 Male, N = 2,6061 Refused to Disclose, N = 71 Transgender, N = 231
CE_SCREENED
DV Referral 307 (7.1%) 39 (1.5%) 1 (14%) 0 (0%)
Literally Homeless 3,148 (73%) 2,273 (87%) 5 (71%) 17 (74%)
Precariously Housed 46 (1.1%) 10 (0.4%) 0 (0%) 0 (0%)
Risk of Homelessness 804 (19%) 274 (11%) 1 (14%) 6 (26%)
ROI Declined 21 (0.5%) 9 (0.3%) 0 (0%) 0 (0%)

1 n (%)

#tbl_by <- tableby(GENDER ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Housing'))
#summary(tbl_by)
#q1.e <- as.data.frame(tbl_by)
  1. Crosstab: CE Screened (all categories) filtered by Need=Information Services x Gender
q1.f <- needs %>% filter(NEED == 'Information Services') %>% select(CONTACT_NUMBER, CE_SCREENED, GENDER) %>% distinct()
q1.f %>% select(CE_SCREENED,GENDER) %>% tbl_summary(by = GENDER,missing = "no")
## 190 observations missing `GENDER` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `GENDER` column before passing to `tbl_summary()`.
Characteristic Female, N = 4,9081 Male, N = 3,7821 Refused to Disclose, N = 181 Transgender, N = 311
CE_SCREENED
DV Referral 35 (0.7%) 5 (0.1%) 0 (0%) 0 (0%)
Literally Homeless 3,706 (76%) 3,326 (88%) 11 (61%) 26 (84%)
Precariously Housed 70 (1.4%) 27 (0.7%) 0 (0%) 1 (3.2%)
Risk of Homelessness 1,088 (22%) 415 (11%) 7 (39%) 4 (13%)
ROI Declined 7 (0.1%) 8 (0.2%) 0 (0%) 0 (0%)

1 n (%)

#tbl_by <- tableby(GENDER ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Information Services'))
#summary(tbl_by)
#q1.f <- as.data.frame(tbl_by)
  1. Crosstab: CE Screened (all categories) filtered by Need=Housing x Disability Status
q1.g <- needs %>% filter(NEED == 'Housing') %>% select(CONTACT_NUMBER, DISABILITY_STATUS, CE_SCREENED) %>% distinct()
q1.g %>% select(DISABILITY_STATUS,CE_SCREENED) %>% tbl_summary(by = DISABILITY_STATUS,missing = "no")
## 156 observations missing `DISABILITY_STATUS` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `DISABILITY_STATUS` column before passing to `tbl_summary()`.
Characteristic No, N = 4,7021 Not Collected, N = 661 Refused to Disclose, N = 171 Yes, N = 2,1601
CE_SCREENED
DV Referral 243 (5.2%) 6 (9.1%) 6 (35%) 78 (3.6%)
Literally Homeless 3,614 (77%) 53 (80%) 10 (59%) 1,759 (81%)
Precariously Housed 38 (0.8%) 0 (0%) 0 (0%) 16 (0.7%)
Risk of Homelessness 781 (17%) 6 (9.1%) 0 (0%) 295 (14%)
ROI Declined 17 (0.4%) 1 (1.5%) 1 (5.9%) 11 (0.5%)

1 n (%)

#tbl_by <- tableby(DISABILITY_STATUS ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Housing'))
#summary(tbl_by)
#q1.g <- as.data.frame(tbl_by)
  1. Crosstab: CE Screened (all categories) filtered by Need=Housing x Disability Status
q1.h <- needs %>% filter(NEED == 'Information Services') %>% select(CONTACT_NUMBER, DISABILITY_STATUS, CE_SCREENED) %>% distinct()
q1.h %>% select(DISABILITY_STATUS,CE_SCREENED) %>% tbl_summary(by = DISABILITY_STATUS,missing = "no")
## 196 observations missing `DISABILITY_STATUS` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `DISABILITY_STATUS` column before passing to `tbl_summary()`.
Characteristic No, N = 5,9181 Not Collected, N = 3711 Refused to Disclose, N = 241 Yes, N = 2,4201
CE_SCREENED
DV Referral 29 (0.5%) 0 (0%) 0 (0%) 10 (0.4%)
Literally Homeless 4,719 (80%) 357 (96%) 22 (92%) 1,967 (81%)
Precariously Housed 71 (1.2%) 2 (0.5%) 0 (0%) 23 (1.0%)
Risk of Homelessness 1,086 (18%) 11 (3.0%) 1 (4.2%) 416 (17%)
ROI Declined 10 (0.2%) 1 (0.3%) 1 (4.2%) 3 (0.1%)

1 n (%)

#tbl_by <- tableby(DISABILITY_STATUS ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Information Services'))
##summary(tbl_by)
#q1.h <- as.data.frame(tbl_by)
  1. Crosstab: CE Screened (all categories) filtered by Need=Housing x MILITARY_STATUS
q1.i <- needs %>% filter(NEED == 'Housing') %>% select(CONTACT_NUMBER, MILITARY_STATUS, CE_SCREENED) %>% distinct()
q1.i %>% select(MILITARY_STATUS,CE_SCREENED) %>% tbl_summary(by = MILITARY_STATUS,missing = "no")
## 207 observations missing `MILITARY_STATUS` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `MILITARY_STATUS` column before passing to `tbl_summary()`.
Characteristic Active, N = 31 No, N = 6,5011 Not Collected, N = 331 Refused to Disclose, N = 131 Veteran, N = 3441
CE_SCREENED
DV Referral 1 (33%) 308 (4.7%) 3 (9.1%) 5 (38%) 9 (2.6%)
Literally Homeless 2 (67%) 5,072 (78%) 24 (73%) 8 (62%) 288 (84%)
Precariously Housed 0 (0%) 50 (0.8%) 2 (6.1%) 0 (0%) 2 (0.6%)
Risk of Homelessness 0 (0%) 1,032 (16%) 3 (9.1%) 0 (0%) 45 (13%)
ROI Declined 0 (0%) 29 (0.4%) 1 (3.0%) 0 (0%) 0 (0%)

1 n (%)

#tbl_by <- tableby(MILITARY_STATUS ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Housing'))
##summary(tbl_by)
#q1.i <- as.data.frame(tbl_by)
  1. Crosstab: CE Screened (all categories) filtered by Need=Housing x Disability Status
q1.j <- needs %>% filter(NEED == 'Information Services') %>% select(CONTACT_NUMBER, MILITARY_STATUS, CE_SCREENED) %>% distinct()
q1.j %>% select(MILITARY_STATUS,CE_SCREENED) %>% tbl_summary(by = MILITARY_STATUS,missing = "no")
## 317 observations missing `MILITARY_STATUS` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `MILITARY_STATUS` column before passing to `tbl_summary()`.
Characteristic Active, N = 141 No, N = 8,0921 Not Collected, N = 251 Refused to Disclose, N = 121 Veteran, N = 4691
CE_SCREENED
DV Referral 0 (0%) 36 (0.4%) 0 (0%) 0 (0%) 3 (0.6%)
Literally Homeless 11 (79%) 6,514 (81%) 14 (56%) 8 (67%) 407 (87%)
Precariously Housed 1 (7.1%) 87 (1.1%) 2 (8.0%) 0 (0%) 3 (0.6%)
Risk of Homelessness 2 (14%) 1,439 (18%) 8 (32%) 3 (25%) 56 (12%)
ROI Declined 0 (0%) 13 (0.2%) 1 (4.0%) 1 (8.3%) 0 (0%)

1 n (%)

#tbl_by <- tableby(MILITARY_STATUS ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Information Services'))
##summary(tbl_by)
#q1.j <- as.data.frame(tbl_by)

Q2 -

q2 <- needs %>% select(CONTACT_NUMBER,GENDER,DISABILITY_STATUS,MILITARY_STATUS,AGE,NEED,REFERRAL) %>% distinct()
q2 %>% select(-c(CONTACT_NUMBER)) %>% tbl_summary(by = REFERRAL,missing = "no")
## 113 observations missing `REFERRAL` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `REFERRAL` column before passing to `tbl_summary()`.
Characteristic 2020 Census, N = 31 211.org, N = 21 411 Directory Assistance, N = 41 AARP, N = 21 Agape Christian Counseling, N = 21 American Cancer Society, N = 21 American Red Cross - Charlotte Metro Chapter, N = 181 Angel House Maternity Home, N = 111 Area Agency on Aging - Centralina, N = 61 Asheville-Buncombe Community Christian Ministry, N = 391 Autism Society of North Carolina - Charlotte, N = 61 Blue Haven, N = 11 C.W. Williams Community Health Center, N = 171 Cabarrus Victims Assistance Network, N = 31 Camino Community Center, N = 31 Campaign for Southern Equality, N = 31 Caramore Community, N = 41 Cardinal Innovations, N = 691 Care Ring, N = 131 Carolinas CARE Partnership, N = 91 Carolinas Medical Center - Carolinas HealthCare System, N = 21 Catherines House, N = 31 Catholic Charities - Charlotte Regional Office, N = 201 Center for Community Transitions, N = 51 Center for Family Violence Prevention, N = 11 Charles George Veterans Affairs Medical Center, N = 11 Charlotte Area Fund, N = 1201 Charlotte Area Transit System, N = 171 Charlotte Berean Seventh Day Adventist Church Community Center, N = 441 Charlotte Center for Legal Advocacy, N = 201 Charlotte Community Health Clinic, N = 71 Charlotte Family Housing, N = 2611 Charlotte Rescue Mission, N = 91 Charlotte Vet Center, N = 21 Charlotte Works, N = 21 Charlottetown Manor, N = 31 Child Care Resources, N = 201 Community Link, N = 5801 Community Shelter of Union County, N = 51 Community Support Services of Mecklenburg County, N = 4,4341 Cooperative Christian Ministry, N = 61 Coronavirus, N = 7011 Crisis Assistance Ministry, N = 1451 CriSyS, N = 21 Davidson Housing Coalition, N = 31 Debt Reduction Services, N = 21 Department of Public Health - Mecklenburg County, N = 131 Department of Social Services - Mecklenburg County, N = 1741 Derita Presbyterian Church child care, N = 31 Dilworth Soup Kitchen, N = 241 Disability Rights and Resources, N = 41 DreamKey Partners, N = 101 Dress for Success - Charlotte, N = 111 Durham Crisis Response Center, N = 11 Easterseals UCP, N = 21 EnergyUnited, N = 361 Esther House, N = 21 Family Promise of Gaston County, N = 51 Family Promise of Wake County, N = 21 FeedNC, N = 171 Fifth Street Ministries, N = 71 Florence Crittenton Services, N = 231 Friendship Trays, N = 51 Gaston Community Action, N = 11 Goodwill - Southern Piedmont, N = 371 Goodwill Career Connections Center - Catawba County Center, N = 61 Government Services - City of Charlotte, N = 91 Government Services - Mecklenburg County, N = 61 Government Services - Scotland County, N = 21 Grocery Worker's Relief Fund, N = 31 Habitat for Humanity - Cabarrus County, N = 21 Habitat for Humanity - Charlotte Region, N = 31 Habitat for Humanity - Greater Matthews, N = 21 Habitat for Humanity - Our Towns, N = 11 Harvest Center of Charlotte, N = 1251 Helping Hand Mission, N = 21 HIV/AIDS Hotline, N = 21 Homes of Hope, N = 21 Hope Haven, N = 21 Hope House Foundation, N = 1451 Hope Street Food Pantry, N = 171 Humane Society - Charlotte, N = 41 If My People Food Pantry, N = 261 Inlivian, N = 1561 Internal Initiatives, N = 11 Internal Revenue Service, N = 21 Jewish Community Services of South Florida, N = 21 Jewish Family Services of Greater Charlotte, N = 31 Latin American Coalition, N = 31 Leading Into New Communities, N = 141 Legal Aid of North Carolina, N = 501 Leukemia and Lymphoma Society, N = 21 Liberty Baptist Church Food Pantry, N = 751 Lifeline, N = 41 LIFESPAN, N = 21 Loaves and Fishes, N = 691 Lois Lodge Maternity Home, N = 31 Lutheran Services Carolinas, N = 11 Matthews Free Medical Clinic, N = 41 Matthews Help Center, N = 141 Mayfield Memorial Apartments, N = 851 McDowell Street Center for Family Law, N = 111 McLeod Addictive Disease Center, N = 91 Mecklenburg County Senior Center, N = 31 MECKLINK Behavioral Healthcare, N = 21 Mens Shelter of Charlotte, N = 3841 Metrolina Association for the Blind, N = 11 MiraVia, N = 171 Mission Hospital, N = 21 Mother of Mercy Catholic Church, N = 31 Mount Olive Presbyterian Church, N = 501 My Sisters Success of North Carolina, N = 11 National Domestic Violence Hotline, N = 1611 Navy-Marine Corps Relief Society, N = 31 NC MedAssist, N = 151 NCHousingSearch.org, N = 7111 NCWorks Career Center - Mecklenburg County, N = 721 NeedyMeds, N = 21 New Outreach Christian Center, N = 331 North Carolina Back@Home Program, N = 231 North Carolina Baptist Aging Ministry, N = 51 North Carolina Bar Association, N = 31 North Carolina Continuum of Care, N = 11,3431 North Carolina Council of the Blind, N = 51 North Carolina Department of Commerce, N = 21 North Carolina Division of Health Service Regulation, N = 41 North Carolina Division of Motor Vehicles, N = 311 North Carolina Division of Motor Vehicles - Mecklenburg County, N = 21 North Carolina Division of Services for the Blind - Charlotte District, N = 31 North Carolina Division of Vocational Rehabilitation - Charlotte, N = 61 North Carolina Emergency Management, N = 21 North Carolina Governor, N = 11 North Carolina HOPE Program, N = 31 North Carolina Housing Finance Agency, N = 21 North Carolina Lions Club, N = 21 North Carolina Marketplace In-Person Assistance, N = 21 North Carolina Medicaid, N = 41 North Carolina Missions of Mercy, N = 41 North Carolina Operated Healthcare Facilities, N = 21 North Carolina Oxford Houses, N = 51 Novant Health - Michael Jordan Family Medical Clinic, N = 21 Open Door Ministries of High Point, N = 11 Operation Home Front, N = 41 P. K. Management, N = 21 Pinecrest Manor, N = 21 Pines at Carolina Place The, N = 41 Plaza Baptist Church Day Care, N = 31 Pregnancy Resource Center of Charlotte, N = 21 Presbyterian Healthcare, N = 51 Primary Health-Care of Charlotte P.A., N = 21 Professionals in Transition, N = 21 Project Outpour, N = 61 Prosperity Unlimited, N = 21 Queen City Worship Center, N = 451 RAIN, N = 71 RAMP Charlotte, N = 1621 Rape Abuse and Incest National Network, N = 31 REAL Crisis Intervention, N = 31 Rescue Missions Ministries, N = 11 Resident Relief Foundation, N = 51 Room at the Inn, N = 91 Rowan Helping Ministries, N = 61 Safe Alliance, N = 5941 Safe Harbor, N = 1941 Safe Haven of Person County, N = 21 Salisbury Rowan Community Action Agency, N = 31 Salvation Army - Cabarrus and Stanly Counties, N = 421 Salvation Army - Greater Charlotte, N = 3701 Salvation Army - Sandhills Region, N = 11 Salvation Army - Wake County, N = 21 Salvation Army Center of Hope - Gaston County, N = 251 School District - Charlotte-Mecklenburg, N = 21 Self-Help Credit Union - Charlotte, N = 51 Selwyn Ave. Presbyterian Church Child Care, N = 31 Servants Heart of Mint Hill, N = 81 Shalom Adonai Church of God, N = 211 Sharon Baptist Church Child Care, N = 31 Sharon Village Retirement Apartments, N = 31 SingleCare Prescription Card, N = 31 St. Paul Baptist Church Food Pantry, N = 481 Substance Abuse and Mental Health Services Administration, N = 21 Supportive Housing Communities, N = 1631 Syrenity House Empowered, N = 111 The Relatives, N = 251 Thrift United Methodist Church, N = 671 Toys for Tots, N = 31 Trans Lifeline, N = 31 Turning Point, N = 451 United States Centers for Disease Control and Prevention, N = 61 United States Department of Veterans Affairs, N = 81 United States Social Security Administration, N = 221 United Way Association of South Carolina, N = 61 United Way of North Carolina, N = 41 University Soup Kitchen, N = 21 Urban League of Central Carolinas, N = 111 Urban Ministry Center, N = 3591 Veterans Bridge Home, N = 421 Victory Christian Center, N = 781 YWCA - Central Carolinas, N = 721
GENDER
Female 3 (100%) 2 (100%) 2 (50%) 2 (100%) 0 (0%) 2 (100%) 11 (61%) 9 (82%) 3 (50%) 12 (31%) 6 (100%) 1 (100%) 6 (35%) 3 (100%) 3 (100%) 0 (0%) 2 (50%) 39 (59%) 9 (69%) 4 (44%) 0 (0%) 3 (100%) 14 (70%) 2 (40%) 1 (100%) 1 (100%) 56 (49%) 14 (82%) 28 (67%) 10 (50%) 5 (71%) 221 (86%) 2 (22%) 0 (0%) 2 (100%) 0 (0%) 18 (90%) 383 (67%) 3 (60%) 2,484 (57%) 6 (100%) 391 (67%) 82 (57%) 2 (100%) 1 (33%) 2 (100%) 3 (23%) 104 (60%) 3 (100%) 11 (46%) 2 (50%) 8 (80%) 11 (100%) 1 (100%) 0 (0%) 23 (64%) 2 (100%) 5 (100%) 2 (100%) 8 (47%) 7 (100%) 21 (91%) 2 (40%) 0 (0%) 13 (38%) 6 (100%) 9 (100%) 3 (50%) 0 (0%) 0 (0%) 2 (100%) 1 (100%) 0 (NA%) 1 (100%) 86 (69%) 2 (100%) 0 (0%) 2 (100%) 0 (0%) 134 (96%) 10 (59%) 2 (50%) 19 (79%) 123 (80%) 1 (100%) 0 (0%) 2 (100%) 2 (67%) 1 (33%) 0 (0%) 34 (68%) 2 (100%) 49 (67%) 0 (0%) 0 (0%) 56 (81%) 3 (100%) 0 (0%) 4 (100%) 11 (79%) 50 (62%) 9 (82%) 5 (56%) 0 (0%) 0 (0%) 20 (5.3%) 0 (0%) 17 (100%) 2 (100%) 3 (100%) 38 (79%) 1 (100%) 152 (94%) 1 (33%) 4 (31%) 479 (69%) 22 (32%) 0 (0%) 23 (70%) 16 (70%) 5 (100%) 3 (100%) 6,446 (58%) 3 (60%) 0 (0%) 0 (0%) 14 (45%) 0 (0%) 0 (0%) 2 (33%) 2 (100%) 1 (100%) 3 (100%) 2 (100%) 0 (0%) 2 (100%) 0 (0%) 0 (0%) 2 (100%) 1 (20%) 0 (0%) 0 (0%) 2 (50%) 2 (100%) 0 (0%) 4 (100%) 0 (0%) 2 (100%) 0 (0%) 0 (0%) 0 (0%) 2 (33%) 2 (100%) 26 (58%) 0 (0%) 134 (83%) 0 (0%) 0 (0%) 0 (0%) 5 (100%) 7 (78%) 2 (33%) 522 (91%) 186 (98%) 2 (100%) 0 (0%) 31 (74%) 344 (94%) 0 (0%) 2 (100%) 19 (76%) 2 (100%) 5 (100%) 3 (100%) 8 (100%) 14 (74%) 3 (100%) 0 (0%) 0 (0%) 31 (67%) 2 (100%) 99 (61%) 11 (100%) 7 (28%) 46 (69%) 3 (100%) 0 (0%) 45 (100%) 6 (100%) 5 (62%) 12 (55%) 6 (100%) 2 (50%) 0 (0%) 5 (45%) 154 (45%) 10 (24%) 52 (67%) 69 (97%)
Male 0 (0%) 0 (0%) 2 (50%) 0 (0%) 2 (100%) 0 (0%) 7 (39%) 2 (18%) 3 (50%) 27 (69%) 0 (0%) 0 (0%) 11 (65%) 0 (0%) 0 (0%) 0 (0%) 2 (50%) 25 (38%) 4 (31%) 5 (56%) 2 (100%) 0 (0%) 6 (30%) 3 (60%) 0 (0%) 0 (0%) 59 (51%) 3 (18%) 14 (33%) 10 (50%) 2 (29%) 37 (14%) 5 (56%) 2 (100%) 0 (0%) 3 (100%) 2 (10%) 185 (32%) 2 (40%) 1,888 (43%) 0 (0%) 190 (32%) 59 (41%) 0 (0%) 2 (67%) 0 (0%) 10 (77%) 68 (40%) 0 (0%) 13 (54%) 2 (50%) 2 (20%) 0 (0%) 0 (0%) 2 (100%) 13 (36%) 0 (0%) 0 (0%) 0 (0%) 9 (53%) 0 (0%) 2 (8.7%) 3 (60%) 1 (100%) 21 (62%) 0 (0%) 0 (0%) 3 (50%) 2 (100%) 3 (100%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 38 (31%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (3.6%) 7 (41%) 2 (50%) 5 (21%) 30 (20%) 0 (0%) 2 (100%) 0 (0%) 1 (33%) 2 (67%) 14 (100%) 16 (32%) 0 (0%) 24 (33%) 4 (100%) 2 (100%) 13 (19%) 0 (0%) 1 (100%) 0 (0%) 3 (21%) 31 (38%) 2 (18%) 4 (44%) 3 (100%) 2 (100%) 354 (95%) 1 (100%) 0 (0%) 0 (0%) 0 (0%) 10 (21%) 0 (0%) 9 (5.6%) 2 (67%) 9 (69%) 217 (31%) 47 (68%) 2 (100%) 10 (30%) 7 (30%) 0 (0%) 0 (0%) 4,609 (41%) 2 (40%) 2 (100%) 4 (100%) 17 (55%) 2 (100%) 3 (100%) 4 (67%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (100%) 0 (0%) 4 (100%) 4 (100%) 0 (0%) 4 (80%) 2 (100%) 1 (100%) 2 (50%) 0 (0%) 2 (100%) 0 (0%) 3 (100%) 0 (0%) 3 (100%) 2 (100%) 2 (100%) 4 (67%) 0 (0%) 14 (31%) 7 (100%) 28 (17%) 3 (100%) 0 (0%) 1 (100%) 0 (0%) 2 (22%) 4 (67%) 48 (8.4%) 3 (1.6%) 0 (0%) 3 (100%) 11 (26%) 20 (5.5%) 1 (100%) 0 (0%) 6 (24%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (26%) 0 (0%) 3 (100%) 3 (100%) 15 (33%) 0 (0%) 64 (39%) 0 (0%) 18 (72%) 21 (31%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 3 (38%) 8 (36%) 0 (0%) 2 (50%) 2 (100%) 6 (55%) 190 (55%) 32 (76%) 26 (33%) 2 (2.8%)
Refused to Disclose 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (3.0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 4 (<0.1%) 0 (0%) 1 (0.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.7%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 20 (0.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
Transgender 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 3 (100%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (22%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 7 (1.2%) 0 (0%) 18 (0.4%) 0 (0%) 3 (0.5%) 3 (2.1%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 2 (100%) 0 (0%) 2 (100%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.1%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 39 (0.4%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (11%) 0 (0%) 0 (0%) 0 (0%) 3 (100%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (0.5%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 3 (100%) 0 (0%) 0 (0%) 0 (0%) 2 (9.1%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (0.6%) 0 (0%) 0 (0%) 0 (0%)
DISABILITY_STATUS
No 1 (33%) 2 (100%) 3 (75%) 2 (100%) 2 (100%) 0 (0%) 11 (61%) 11 (100%) 3 (50%) 20 (53%) 2 (50%) 0 (0%) 9 (53%) 2 (67%) 3 (100%) 3 (100%) 4 (100%) 35 (53%) 7 (54%) 7 (78%) 2 (100%) 3 (100%) 19 (95%) 5 (100%) 0 (NA%) 1 (100%) 87 (74%) 7 (41%) 27 (64%) 9 (45%) 5 (71%) 204 (79%) 7 (78%) 2 (100%) 2 (100%) 0 (0%) 12 (60%) 375 (65%) 4 (80%) 2,928 (67%) 6 (100%) 437 (75%) 92 (63%) 2 (100%) 1 (33%) 2 (100%) 9 (69%) 80 (47%) 0 (0%) 17 (71%) 2 (50%) 8 (80%) 6 (55%) 0 (0%) 0 (0%) 22 (61%) 1 (100%) 5 (100%) 2 (100%) 9 (53%) 7 (100%) 16 (70%) 0 (0%) 1 (100%) 22 (65%) 4 (67%) 9 (100%) 3 (50%) 0 (0%) 0 (0%) 2 (100%) 1 (100%) 0 (NA%) 1 (100%) 101 (81%) 2 (100%) 0 (0%) 0 (0%) 0 (0%) 108 (76%) 12 (71%) 4 (100%) 13 (54%) 97 (62%) 1 (100%) 2 (100%) 2 (100%) 2 (67%) 1 (33%) 6 (43%) 27 (54%) 2 (100%) 45 (62%) 4 (100%) 0 (0%) 52 (75%) 3 (100%) 1 (100%) 2 (50%) 13 (93%) 18 (22%) 7 (64%) 7 (78%) 0 (0%) 0 (0%) 267 (72%) 1 (100%) 17 (100%) 2 (100%) 1 (33%) 27 (56%) 1 (100%) 114 (72%) 3 (100%) 2 (15%) 461 (66%) 53 (77%) 0 (0%) 23 (70%) 14 (61%) 3 (60%) 0 (0%) 7,503 (68%) 0 (0%) 2 (100%) 2 (50%) 15 (48%) 2 (100%) 0 (0%) 2 (33%) 1 (50%) 1 (100%) 3 (100%) 0 (0%) 2 (100%) 2 (100%) 2 (50%) 2 (50%) 0 (0%) 5 (100%) 0 (0%) 1 (100%) 0 (0%) 0 (0%) 2 (100%) 2 (50%) 3 (100%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 4 (67%) 0 (0%) 28 (62%) 3 (43%) 123 (76%) 3 (100%) 3 (100%) 1 (100%) 1 (20%) 9 (100%) 4 (67%) 418 (76%) 152 (79%) 1 (50%) 3 (100%) 28 (67%) 257 (71%) 1 (100%) 2 (100%) 21 (84%) 0 (0%) 5 (100%) 0 (0%) 4 (50%) 11 (58%) 0 (0%) 0 (0%) 0 (0%) 26 (57%) 2 (100%) 95 (58%) 9 (82%) 18 (72%) 39 (58%) 3 (100%) 3 (100%) 23 (61%) 4 (67%) 3 (38%) 10 (45%) 4 (67%) 3 (100%) 0 (0%) 5 (45%) 229 (66%) 28 (67%) 40 (51%) 52 (72%)
Not Collected 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (3.0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 1 (0.8%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (0.8%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 21 (0.5%) 0 (0%) 1 (0.2%) 1 (0.7%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (2.9%) 0 (0%) 2 (8.3%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (12%) 0 (0%) 2 (8.7%) 0 (0%) 0 (0%) 2 (5.9%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (67%) 0 (0%) 2 (4.0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (18%) 0 (0%) 0 (0%) 0 (0%) 4 (1.1%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 4 (0.6%) 2 (2.9%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 409 (3.7%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (100%) 2 (33%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 11 (2.0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 3 (0.8%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (5.3%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (100%) 0 (0%) 9 (2.6%) 0 (0%) 0 (0%) 0 (0%)
Refused to Disclose 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (5.0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.2%) 0 (0%) 10 (0.2%) 0 (0%) 1 (0.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (2.0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 25 (0.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (0.9%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
Yes 2 (67%) 0 (0%) 1 (25%) 0 (0%) 0 (0%) 2 (100%) 7 (39%) 0 (0%) 3 (50%) 18 (47%) 2 (50%) 1 (100%) 8 (47%) 1 (33%) 0 (0%) 0 (0%) 0 (0%) 29 (44%) 6 (46%) 2 (22%) 0 (0%) 0 (0%) 1 (5.0%) 0 (0%) 0 (NA%) 0 (0%) 30 (25%) 10 (59%) 15 (36%) 10 (50%) 2 (29%) 52 (20%) 2 (22%) 0 (0%) 0 (0%) 3 (100%) 8 (40%) 197 (34%) 1 (20%) 1,433 (33%) 0 (0%) 142 (24%) 52 (36%) 0 (0%) 2 (67%) 0 (0%) 4 (31%) 87 (51%) 3 (100%) 5 (21%) 2 (50%) 2 (20%) 5 (45%) 1 (100%) 2 (100%) 14 (39%) 0 (0%) 0 (0%) 0 (0%) 6 (35%) 0 (0%) 5 (22%) 5 (100%) 0 (0%) 10 (29%) 2 (33%) 0 (0%) 3 (50%) 2 (100%) 3 (100%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 23 (19%) 0 (0%) 2 (100%) 2 (100%) 2 (100%) 35 (24%) 5 (29%) 0 (0%) 11 (46%) 59 (38%) 0 (0%) 0 (0%) 0 (0%) 1 (33%) 0 (0%) 8 (57%) 20 (40%) 0 (0%) 28 (38%) 0 (0%) 2 (100%) 17 (25%) 0 (0%) 0 (0%) 2 (50%) 1 (7.1%) 63 (78%) 2 (18%) 2 (22%) 3 (100%) 2 (100%) 102 (27%) 0 (0%) 0 (0%) 0 (0%) 2 (67%) 21 (44%) 0 (0%) 44 (28%) 0 (0%) 11 (85%) 235 (34%) 14 (20%) 2 (100%) 10 (30%) 9 (39%) 2 (40%) 3 (100%) 3,170 (29%) 5 (100%) 0 (0%) 2 (50%) 16 (52%) 0 (0%) 3 (100%) 4 (67%) 1 (50%) 0 (0%) 0 (0%) 2 (100%) 0 (0%) 0 (0%) 2 (50%) 2 (50%) 2 (100%) 0 (0%) 2 (100%) 0 (0%) 4 (100%) 2 (100%) 0 (0%) 2 (50%) 0 (0%) 2 (100%) 3 (100%) 2 (100%) 0 (0%) 0 (0%) 2 (100%) 17 (38%) 4 (57%) 39 (24%) 0 (0%) 0 (0%) 0 (0%) 4 (80%) 0 (0%) 2 (33%) 119 (22%) 40 (21%) 1 (50%) 0 (0%) 14 (33%) 103 (28%) 0 (0%) 0 (0%) 4 (16%) 2 (100%) 0 (0%) 3 (100%) 4 (50%) 8 (42%) 3 (100%) 3 (100%) 3 (100%) 20 (43%) 0 (0%) 68 (42%) 2 (18%) 7 (28%) 28 (42%) 0 (0%) 0 (0%) 13 (34%) 2 (33%) 5 (62%) 12 (55%) 2 (33%) 0 (0%) 0 (0%) 6 (55%) 108 (31%) 14 (33%) 38 (49%) 20 (28%)
MILITARY_STATUS
Active 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (<0.1%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 14 (0.1%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
No 3 (100%) 2 (100%) 4 (100%) 2 (100%) 2 (100%) 2 (100%) 15 (83%) 8 (73%) 6 (100%) 8 (21%) 2 (50%) 1 (100%) 17 (100%) 3 (100%) 3 (100%) 3 (100%) 4 (100%) 57 (92%) 13 (100%) 9 (100%) 2 (100%) 2 (67%) 18 (90%) 5 (100%) 0 (NA%) 0 (0%) 114 (97%) 12 (80%) 40 (95%) 17 (89%) 7 (100%) 243 (95%) 7 (100%) 0 (0%) 2 (100%) 0 (0%) 20 (100%) 535 (94%) 5 (100%) 4,107 (94%) 6 (100%) 559 (96%) 143 (99%) 2 (100%) 3 (100%) 2 (100%) 13 (100%) 159 (95%) 3 (100%) 22 (92%) 4 (100%) 8 (80%) 11 (100%) 1 (100%) 2 (100%) 32 (89%) 1 (100%) 5 (100%) 2 (100%) 11 (65%) 7 (100%) 20 (87%) 5 (100%) 1 (100%) 29 (85%) 4 (100%) 7 (78%) 3 (50%) 0 (0%) 3 (100%) 2 (100%) 1 (100%) 0 (NA%) 1 (100%) 123 (99%) 2 (100%) 0 (NA%) 2 (100%) 0 (NA%) 137 (96%) 17 (100%) 4 (100%) 24 (100%) 154 (99%) 1 (100%) 2 (100%) 2 (100%) 3 (100%) 1 (33%) 14 (100%) 42 (86%) 2 (100%) 66 (90%) 4 (100%) 2 (100%) 60 (87%) 0 (0%) 1 (100%) 4 (100%) 14 (100%) 79 (98%) 9 (82%) 9 (100%) 3 (100%) 2 (100%) 340 (93%) 1 (100%) 14 (82%) 2 (100%) 3 (100%) 45 (94%) 1 (100%) 150 (95%) 0 (0%) 13 (100%) 641 (93%) 64 (93%) 2 (100%) 33 (100%) 21 (91%) 5 (100%) 3 (100%) 10,303 (94%) 5 (100%) 2 (100%) 4 (100%) 26 (84%) 2 (100%) 3 (100%) 4 (100%) 2 (100%) 1 (100%) 3 (100%) 2 (100%) 2 (100%) 2 (100%) 4 (100%) 4 (100%) 2 (100%) 5 (100%) 2 (100%) 0 (0%) 0 (0%) 2 (100%) 2 (100%) 4 (100%) 3 (100%) 2 (100%) 3 (100%) 2 (100%) 0 (0%) 4 (67%) 2 (100%) 40 (89%) 7 (100%) 159 (98%) 3 (100%) 3 (100%) 1 (100%) 5 (100%) 6 (67%) 4 (67%) 517 (95%) 192 (100%) 2 (100%) 3 (100%) 33 (79%) 341 (97%) 1 (100%) 2 (100%) 25 (100%) 2 (100%) 5 (100%) 3 (100%) 8 (100%) 19 (100%) 3 (100%) 0 (0%) 3 (100%) 43 (93%) 2 (100%) 155 (97%) 11 (100%) 23 (92%) 59 (88%) 3 (100%) 3 (100%) 37 (97%) 5 (83%) 0 (0%) 16 (80%) 4 (67%) 4 (100%) 0 (0%) 11 (100%) 315 (92%) 0 (0%) 78 (100%) 69 (97%)
Not Collected 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (50%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (0.8%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 9 (0.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (1.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (4.1%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (18%) 0 (0%) 0 (0%) 0 (0%) 3 (0.8%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 44 (0.4%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (0.9%) 0 (0%) 0 (0%) 0 (0%) 2 (4.8%) 5 (1.4%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (2.6%) 0 (0%) 0 (0%) 2 (10%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (1.5%) 0 (0%) 0 (0%) 0 (0%)
Refused to Disclose 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 8 (0.2%) 0 (0%) 1 (0.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (22%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 11 (0.1%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 4 (0.7%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
Veteran 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 3 (17%) 3 (27%) 0 (0%) 31 (79%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (8.1%) 0 (0%) 0 (0%) 0 (0%) 1 (33%) 2 (10%) 0 (0%) 0 (NA%) 1 (100%) 4 (3.4%) 3 (20%) 2 (4.8%) 2 (11%) 0 (0%) 10 (3.9%) 0 (0%) 2 (100%) 0 (0%) 3 (100%) 0 (0%) 32 (5.6%) 0 (0%) 245 (5.6%) 0 (0%) 22 (3.8%) 1 (0.7%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 7 (4.2%) 0 (0%) 2 (8.3%) 0 (0%) 2 (20%) 0 (0%) 0 (0%) 0 (0%) 4 (11%) 0 (0%) 0 (0%) 0 (0%) 6 (35%) 0 (0%) 3 (13%) 0 (0%) 0 (0%) 5 (15%) 0 (0%) 0 (0%) 3 (50%) 2 (100%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 1 (0.8%) 0 (0%) 0 (NA%) 0 (0%) 0 (NA%) 6 (4.2%) 0 (0%) 0 (0%) 0 (0%) 2 (1.3%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (67%) 0 (0%) 5 (10%) 0 (0%) 7 (9.6%) 0 (0%) 0 (0%) 9 (13%) 3 (100%) 0 (0%) 0 (0%) 0 (0%) 2 (2.5%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 22 (6.0%) 0 (0%) 3 (18%) 0 (0%) 0 (0%) 3 (6.2%) 0 (0%) 8 (5.1%) 3 (100%) 0 (0%) 49 (7.1%) 5 (7.2%) 0 (0%) 0 (0%) 2 (8.7%) 0 (0%) 0 (0%) 602 (5.5%) 0 (0%) 0 (0%) 0 (0%) 5 (16%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (100%) 4 (100%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (100%) 2 (33%) 0 (0%) 5 (11%) 0 (0%) 3 (1.9%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 3 (33%) 2 (33%) 16 (2.9%) 0 (0%) 0 (0%) 0 (0%) 7 (17%) 7 (2.0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 3 (100%) 0 (0%) 3 (6.5%) 0 (0%) 5 (3.1%) 0 (0%) 2 (8.0%) 8 (12%) 0 (0%) 0 (0%) 0 (0%) 1 (17%) 8 (100%) 2 (10%) 2 (33%) 0 (0%) 2 (100%) 0 (0%) 22 (6.4%) 42 (100%) 0 (0%) 2 (2.8%)
AGE
17 - younger 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 4 (0.7%) 0 (0%) 7 (0.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (20%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 3 (2.1%) 0 (0%) 0 (0%) 0 (0%) 2 (1.3%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 4 (0.6%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 35 (0.3%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 3 (100%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.2%) 5 (2.6%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (20%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
18 - 59 3 (100%) 2 (100%) 4 (100%) 2 (100%) 2 (100%) 2 (100%) 15 (83%) 11 (100%) 0 (0%) 37 (97%) 2 (50%) 1 (100%) 16 (94%) 3 (100%) 3 (100%) 3 (100%) 4 (100%) 64 (97%) 11 (85%) 9 (100%) 2 (100%) 2 (67%) 20 (100%) 5 (100%) 0 (NA%) 1 (100%) 111 (94%) 12 (71%) 42 (100%) 19 (95%) 7 (100%) 249 (98%) 9 (100%) 2 (100%) 2 (100%) 0 (0%) 20 (100%) 529 (92%) 5 (100%) 4,031 (92%) 6 (100%) 536 (93%) 139 (96%) 2 (100%) 1 (33%) 2 (100%) 13 (100%) 149 (87%) 3 (100%) 20 (83%) 2 (50%) 8 (80%) 11 (100%) 1 (100%) 2 (100%) 33 (92%) 1 (100%) 5 (100%) 2 (100%) 15 (88%) 7 (100%) 23 (100%) 2 (40%) 1 (100%) 32 (94%) 6 (100%) 9 (100%) 6 (100%) 0 (0%) 3 (100%) 2 (100%) 1 (100%) 0 (NA%) 1 (100%) 118 (95%) 2 (100%) 2 (100%) 2 (100%) 2 (100%) 133 (93%) 17 (100%) 2 (50%) 22 (85%) 144 (92%) 0 (0%) 2 (100%) 2 (100%) 3 (100%) 1 (33%) 10 (71%) 36 (72%) 2 (100%) 69 (95%) 4 (100%) 2 (100%) 69 (100%) 3 (100%) 1 (100%) 4 (100%) 14 (100%) 51 (63%) 9 (82%) 7 (78%) 0 (0%) 2 (100%) 347 (93%) 0 (0%) 17 (100%) 2 (100%) 3 (100%) 48 (100%) 1 (100%) 154 (97%) 3 (100%) 11 (85%) 625 (89%) 64 (93%) 0 (0%) 33 (100%) 22 (96%) 0 (0%) 3 (100%) 10,153 (91%) 5 (100%) 2 (100%) 4 (100%) 26 (84%) 2 (100%) 3 (100%) 4 (67%) 2 (100%) 1 (100%) 0 (0%) 0 (0%) 2 (100%) 2 (100%) 2 (50%) 4 (100%) 2 (100%) 5 (100%) 2 (100%) 0 (0%) 3 (75%) 2 (100%) 0 (0%) 4 (100%) 3 (100%) 2 (100%) 0 (0%) 2 (100%) 0 (0%) 4 (67%) 0 (0%) 43 (96%) 7 (100%) 159 (98%) 3 (100%) 3 (100%) 1 (100%) 5 (100%) 9 (100%) 5 (83%) 525 (95%) 184 (96%) 2 (100%) 3 (100%) 41 (98%) 346 (95%) 1 (100%) 2 (100%) 23 (92%) 2 (100%) 5 (100%) 3 (100%) 6 (75%) 15 (79%) 3 (100%) 0 (0%) 3 (100%) 46 (96%) 2 (100%) 151 (93%) 11 (100%) 18 (72%) 63 (94%) 3 (100%) 3 (100%) 37 (97%) 6 (100%) 8 (100%) 18 (82%) 4 (67%) 3 (100%) 0 (0%) 11 (100%) 320 (93%) 34 (81%) 73 (94%) 64 (90%)
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65 - older 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 3 (17%) 0 (0%) 6 (100%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (15%) 0 (0%) 0 (0%) 1 (33%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 5 (4.2%) 2 (12%) 0 (0%) 0 (0%) 0 (0%) 2 (0.8%) 0 (0%) 0 (0%) 0 (0%) 3 (100%) 0 (0%) 18 (3.1%) 0 (0%) 141 (3.2%) 0 (0%) 16 (2.8%) 3 (2.1%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 8 (4.7%) 0 (0%) 0 (0%) 2 (50%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (100%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 2 (1.6%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 3 (2.1%) 0 (0%) 2 (50%) 2 (7.7%) 2 (1.3%) 1 (100%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 4 (8.0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 15 (19%) 0 (0%) 0 (0%) 3 (100%) 0 (0%) 3 (0.8%) 1 (100%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (1.3%) 0 (0%) 0 (0%) 35 (5.0%) 0 (0%) 0 (0%) 0 (0%) 1 (4.3%) 3 (60%) 0 (0%) 317 (2.9%) 0 (0%) 0 (0%) 0 (0%) 3 (9.7%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (100%) 0 (0%) 0 (0%) 2 (50%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (25%) 0 (0%) 2 (100%) 0 (0%) 0 (0%) 0 (0%) 1 (33%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (1.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 7 (1.3%) 3 (1.6%) 0 (0%) 0 (0%) 0 (0%) 3 (0.8%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 3 (100%) 0 (0%) 2 (4.2%) 0 (0%) 6 (3.7%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (9.1%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 11 (3.2%) 3 (7.1%) 2 (2.6%) 0 (0%)
Not Collected 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (50%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 9 (0.2%) 0 (0%) 4 (0.7%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (1.2%) 0 (0%) 2 (8.3%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (12%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (5.9%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (67%) 0 (0%) 2 (4.0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (18%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (2.9%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 46 (0.4%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (100%) 2 (33%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 9 (1.6%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (2.6%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (100%) 0 (0%) 4 (1.2%) 0 (0%) 0 (0%) 0 (0%)
Refused to Disclose 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (5.0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 11 (0.3%) 0 (0%) 1 (0.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (NA%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (2.0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.1%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 19 (0.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 3 (0.5%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
NEED
Housing 1 (33%) 1 (50%) 0 (0%) 1 (50%) 0 (0%) 0 (0%) 7 (39%) 4 (36%) 2 (33%) 21 (54%) 3 (50%) 1 (100%) 4 (24%) 3 (100%) 1 (33%) 1 (33%) 1 (25%) 21 (30%) 4 (31%) 2 (22%) 0 (0%) 2 (67%) 5 (25%) 2 (40%) 1 (100%) 1 (100%) 34 (28%) 4 (24%) 13 (30%) 5 (25%) 2 (29%) 160 (61%) 0 (0%) 0 (0%) 0 (0%) 1 (33%) 6 (30%) 287 (49%) 4 (80%) 3,994 (90%) 3 (50%) 365 (52%) 63 (43%) 1 (50%) 2 (67%) 1 (50%) 4 (31%) 50 (29%) 1 (33%) 6 (25%) 1 (25%) 5 (50%) 3 (27%) 1 (100%) 1 (50%) 6 (17%) 2 (100%) 3 (60%) 1 (50%) 4 (24%) 5 (71%) 6 (26%) 2 (40%) 0 (0%) 10 (27%) 0 (0%) 3 (33%) 2 (33%) 1 (50%) 1 (33%) 2 (100%) 2 (67%) 1 (50%) 1 (100%) 62 (50%) 1 (50%) 0 (0%) 1 (50%) 0 (0%) 74 (51%) 6 (35%) 2 (50%) 5 (19%) 84 (54%) 0 (0%) 0 (0%) 1 (50%) 1 (33%) 1 (33%) 3 (21%) 14 (28%) 0 (0%) 19 (25%) 0 (0%) 1 (50%) 17 (25%) 1 (33%) 1 (100%) 2 (50%) 6 (43%) 50 (59%) 3 (27%) 1 (11%) 1 (33%) 0 (0%) 296 (77%) 0 (0%) 4 (24%) 1 (50%) 2 (67%) 12 (24%) 0 (0%) 61 (38%) 2 (67%) 4 (27%) 406 (57%) 18 (25%) 1 (50%) 10 (30%) 21 (91%) 1 (20%) 1 (33%) 2,074 (18%) 1 (20%) 0 (0%) 2 (50%) 7 (23%) 0 (0%) 1 (33%) 1 (17%) 0 (0%) 0 (0%) 1 (33%) 1 (50%) 1 (50%) 1 (50%) 1 (25%) 1 (25%) 0 (0%) 0 (0%) 1 (50%) 1 (100%) 3 (75%) 1 (50%) 1 (50%) 2 (50%) 1 (33%) 1 (50%) 2 (40%) 1 (50%) 1 (50%) 1 (17%) 1 (50%) 10 (22%) 2 (29%) 90 (56%) 1 (33%) 1 (33%) 1 (100%) 3 (60%) 4 (44%) 4 (67%) 397 (67%) 93 (48%) 2 (100%) 1 (33%) 28 (67%) 276 (75%) 1 (100%) 1 (50%) 12 (48%) 1 (50%) 2 (40%) 1 (33%) 0 (0%) 4 (19%) 1 (33%) 1 (33%) 1 (33%) 11 (23%) 1 (50%) 88 (54%) 6 (55%) 12 (48%) 18 (27%) 1 (33%) 1 (33%) 23 (51%) 5 (83%) 3 (38%) 5 (23%) 2 (33%) 0 (0%) 1 (50%) 2 (18%) 190 (53%) 8 (19%) 17 (22%) 42 (58%)
Information Services 2 (67%) 1 (50%) 3 (75%) 0 (0%) 1 (50%) 1 (50%) 3 (17%) 2 (18%) 2 (33%) 15 (38%) 0 (0%) 0 (0%) 5 (29%) 0 (0%) 1 (33%) 1 (33%) 1 (25%) 13 (19%) 3 (23%) 3 (33%) 1 (50%) 1 (33%) 6 (30%) 1 (20%) 0 (0%) 0 (0%) 25 (21%) 5 (29%) 10 (23%) 6 (30%) 2 (29%) 87 (33%) 4 (44%) 1 (50%) 1 (50%) 1 (33%) 6 (30%) 216 (37%) 1 (20%) 272 (6.1%) 3 (50%) 177 (25%) 39 (27%) 0 (0%) 0 (0%) 0 (0%) 4 (31%) 41 (24%) 1 (33%) 7 (29%) 1 (25%) 4 (40%) 4 (36%) 0 (0%) 0 (0%) 10 (28%) 0 (0%) 2 (40%) 1 (50%) 5 (29%) 1 (14%) 6 (26%) 1 (20%) 0 (0%) 7 (19%) 3 (50%) 4 (44%) 2 (33%) 0 (0%) 1 (33%) 0 (0%) 1 (33%) 1 (50%) 0 (0%) 52 (42%) 1 (50%) 1 (50%) 1 (50%) 1 (50%) 58 (40%) 3 (18%) 0 (0%) 9 (35%) 58 (37%) 1 (100%) 1 (50%) 1 (50%) 0 (0%) 0 (0%) 4 (29%) 13 (26%) 1 (50%) 22 (29%) 2 (50%) 0 (0%) 22 (32%) 1 (33%) 0 (0%) 0 (0%) 4 (29%) 29 (34%) 3 (27%) 3 (33%) 1 (33%) 1 (50%) 62 (16%) 0 (0%) 6 (35%) 0 (0%) 0 (0%) 16 (32%) 0 (0%) 15 (9.3%) 0 (0%) 4 (27%) 232 (33%) 20 (28%) 0 (0%) 6 (18%) 0 (0%) 2 (40%) 1 (33%) 8,821 (78%) 2 (40%) 1 (50%) 2 (50%) 10 (32%) 1 (50%) 1 (33%) 2 (33%) 2 (100%) 0 (0%) 1 (33%) 1 (50%) 0 (0%) 0 (0%) 1 (25%) 1 (25%) 1 (50%) 2 (40%) 0 (0%) 0 (0%) 1 (25%) 1 (50%) 1 (50%) 2 (50%) 1 (33%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (33%) 1 (50%) 17 (38%) 2 (29%) 53 (33%) 1 (33%) 1 (33%) 0 (0%) 0 (0%) 1 (11%) 2 (33%) 92 (15%) 84 (43%) 0 (0%) 1 (33%) 13 (31%) 69 (19%) 0 (0%) 1 (50%) 12 (48%) 0 (0%) 1 (20%) 1 (33%) 4 (50%) 7 (33%) 1 (33%) 1 (33%) 1 (33%) 17 (35%) 0 (0%) 65 (40%) 3 (27%) 10 (40%) 20 (30%) 0 (0%) 1 (33%) 6 (13%) 0 (0%) 2 (25%) 6 (27%) 3 (50%) 4 (100%) 0 (0%) 3 (27%) 105 (29%) 19 (45%) 26 (33%) 23 (32%)
Others 0 (0%) 0 (0%) 1 (25%) 1 (50%) 1 (50%) 1 (50%) 8 (44%) 5 (45%) 2 (33%) 3 (7.7%) 3 (50%) 0 (0%) 8 (47%) 0 (0%) 1 (33%) 1 (33%) 2 (50%) 35 (51%) 6 (46%) 4 (44%) 1 (50%) 0 (0%) 9 (45%) 2 (40%) 0 (0%) 0 (0%) 61 (51%) 8 (47%) 21 (48%) 9 (45%) 3 (43%) 14 (5.4%) 5 (56%) 1 (50%) 1 (50%) 1 (33%) 8 (40%) 77 (13%) 0 (0%) 168 (3.8%) 0 (0%) 159 (23%) 43 (30%) 1 (50%) 1 (33%) 1 (50%) 5 (38%) 83 (48%) 1 (33%) 11 (46%) 2 (50%) 1 (10%) 4 (36%) 0 (0%) 1 (50%) 20 (56%) 0 (0%) 0 (0%) 0 (0%) 8 (47%) 1 (14%) 11 (48%) 2 (40%) 1 (100%) 20 (54%) 3 (50%) 2 (22%) 2 (33%) 1 (50%) 1 (33%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 11 (8.8%) 0 (0%) 1 (50%) 0 (0%) 1 (50%) 13 (9.0%) 8 (47%) 2 (50%) 12 (46%) 14 (9.0%) 0 (0%) 1 (50%) 0 (0%) 2 (67%) 2 (67%) 7 (50%) 23 (46%) 1 (50%) 34 (45%) 2 (50%) 1 (50%) 30 (43%) 1 (33%) 0 (0%) 2 (50%) 4 (29%) 6 (7.1%) 5 (45%) 5 (56%) 1 (33%) 1 (50%) 26 (6.8%) 1 (100%) 7 (41%) 1 (50%) 1 (33%) 22 (44%) 1 (100%) 85 (53%) 1 (33%) 7 (47%) 73 (10%) 34 (47%) 1 (50%) 17 (52%) 2 (8.7%) 2 (40%) 1 (33%) 448 (3.9%) 2 (40%) 1 (50%) 0 (0%) 14 (45%) 1 (50%) 1 (33%) 3 (50%) 0 (0%) 1 (100%) 1 (33%) 0 (0%) 1 (50%) 1 (50%) 2 (50%) 2 (50%) 1 (50%) 3 (60%) 1 (50%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (33%) 1 (50%) 3 (60%) 1 (50%) 1 (50%) 3 (50%) 0 (0%) 18 (40%) 3 (43%) 19 (12%) 1 (33%) 1 (33%) 0 (0%) 2 (40%) 4 (44%) 0 (0%) 105 (18%) 17 (8.8%) 0 (0%) 1 (33%) 1 (2.4%) 25 (6.8%) 0 (0%) 0 (0%) 1 (4.0%) 1 (50%) 2 (40%) 1 (33%) 4 (50%) 10 (48%) 1 (33%) 1 (33%) 1 (33%) 20 (42%) 1 (50%) 10 (6.1%) 2 (18%) 3 (12%) 29 (43%) 2 (67%) 1 (33%) 16 (36%) 1 (17%) 3 (38%) 11 (50%) 1 (17%) 0 (0%) 1 (50%) 6 (55%) 64 (18%) 15 (36%) 35 (45%) 7 (9.7%)

1 n (%)

#write.csv(q2,"../Data/Output/Q2.csv",row.names = F)